Knowledge Representation and Sharing Using Visual Semantic Modeling for Diagnostic Medical Image Databases

Barb, Adrian S.; Shyu, Chi‐Ren; Sethi, Yash

doi:10.1109/titb.2005.855563

Cited by 30 publications

(25 citation statements)

References 49 publications

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“…One notable example is the Essence framework, 71 serving as a knowledge repository and exchange platform for medical image databases. In Essence, visual abnormalities and pathologies are extracted and mapped to physician-defined semantic terms using a shared ontology based on the common knowledge from expert radiologists and information from medical references.…”

Section: Challenges and Opportunities For Cbir In Radiologymentioning

confidence: 99%

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

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Section: Challenges and Opportunities For Cbir In Radiologymentioning

confidence: 99%

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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“…Although these studies illustrate that low-level image features can be used to distinguish between malignant and benign nodules, it is important to incorporate radiologists' knowledge into the process and to understand the relationship between the image features and radiologists' annotations. Such understanding can not only improve diagnosis of malignant lung nodules, but also simplify and accelerate the radiology interpretation process as suggested by Kahn et al [10] In the medical imaging area, efforts to find the relationship between image features and subjective or semantic ratings were spearheaded by Barb et al [5], Raicu et al [19], and Samala et al [21] . Barb developed a framework that manages visual content of lung pathologies.…”

Section: Related Workmentioning

confidence: 99%

An investigation into the relationship between semantic and content based similarity using LIDC

Kim

Dasovich

Bhaumik

et al. 2010

Proceedings of the International Conference on Multimedia Information Retrieval

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There is considerable research in the field of content-based medical image retrieval; however, few of the current systems investigate the relationship between the radiologists' visual impression of image similarity and the computer calculated content-based similarity. Furthermore, those research studies that investigate these relationships analyze the visual similarity with respect to degree of malignancy without including specific characteristics that are important in the diagnosis process.The creation of the NIH/NCI Lung Image Database Consortium (LIDC) dataset offers the opportunity to perform the proposed research. Each nodule out of the 932 distinct nodules (larger than 3mm in diameter) was delineated and annotated by up to four radiologists using nine semantic characteristics that are important in the lung nodule interpretation process. Using the LIDC images, we propose to encode the radiologists' characteristic-based similarity and further discover if there is any relationship between this conceptual/characteristic-based similarity and the contentbased similarity for lung nodule interpretation.Our preliminary results show that it is a challenging problem to model the characteristic-based and content-based relationships for a broad category of lung nodules. A correlation of only 0.1 was obtained between the characteristicbased similarity and the predicted characteristic-based similarity using an artificial neural networked trained on four types of low-level image features (size, intensity, shape, and texture) calculated for 640 random pairs of nodules. Future research is necessary to investigate the appropriateness of the considered image features to model both the variation in the human interpretation of the lung nodules and the perceived characteristic-based similarity.

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“…Although these studies illustrate that low-level image features can be used to distinguish between malignant and benign nodules, it is important to incorporate radiologists' knowledge into the process and to understand the relationship between the image features and radiologists' annotations. Such understanding can not only improve diagnosis of malignant lung nodules, but also simplify and accelerate the radiology interpretation process as suggested by Kahn et al 6 In the medical imaging area, efforts to find the relationship between image features and subjective or semantic ratings were spearheaded by Barb et al 7 and Raicu et al 8 Barb developed a framework that manages visual content of lung pathologies. The framework named Evolutionary System for Semantic Exchange of Information in Collaborative Environments (ESSENCE) uses semantic methods to describe visual abnormalities and exchange knowledge in the medical domain.…”

Section: Related Workmentioning

confidence: 99%

A model for the relationship between semantic and content based similarity using LIDC

Dasovich¹,

Kim

Raicu

et al. 2010

SPIE Proceedings

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There is considerable research in the field of content-based image retrieval (CBIR); however, few of the current systems incorporate radiologists' visual impression of image similarity. Our objective is to bridge the semantic gap between radiologists' ratings and image features. We have been developing a conceptual-based similarity model derived from content-based similarity to improve CBIR. Previous work in our lab reduced the Lung Image Database Consortium (LIDC) data set into a selection of 149 images of unique nodules, each containing nine semantic ratings by four radiologists and 64 computed image features. After evaluating the similarity measures for both content-based and semantic-based features, we selected 116 nodule pairs with a high correlation between both similarities. These pairs were used to generate a linear regression model that predicts semantic similarity with content similarity input with an R 2 value of 0.871. The characteristics and features of nodules that were used for the model were also investigated.

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Knowledge Representation and Sharing Using Visual Semantic Modeling for Diagnostic Medical Image Databases

Cited by 30 publications

References 49 publications

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

An investigation into the relationship between semantic and content based similarity using LIDC

A model for the relationship between semantic and content based similarity using LIDC

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